Spack spec

1. What is a spack spec?
2. What is a spack environment?
3. What is spack used for?
4. How do I know what version of spack I have?
5. What is alternative to spack?
6. How do I install spack on Windows?
7. Why do we use DataFrame in spark?
8. How do I update my spack package?
9. What is spark advantage?
10. What is spark in Hadoop?
11. What is the weakness of Spark?
12. Why is Spark so powerful?
13. Why is Spark better than Hadoop?

What is a spack spec?

In Spack, that descriptor is called a spec. Spack uses specs to refer to a particular build configuration (or configurations) of a package. Specs are more than a package name and a version; you can use them to specify the compiler, compiler version, architecture, compile options, and dependency options for a build.

What is a spack environment?

yaml) An environment is used to group together a set of specs for the purpose of building, rebuilding and deploying in a coherent fashion.

What is spack used for?

Spack is a package manager for supercomputers, Linux, and macOS. It makes installing scientific software easy. Spack isn't tied to a particular language; you can build a software stack in Python or R, link to libraries written in C, C++, or Fortran, and easily swap compilers or target specific microarchitectures.

How do I know what version of spack I have?

To get more details for a given package, we can use spack info command. This command gives all the info about the package, variants, dependencies, etc. To check the available versions of a given package, we can use spack versions <package name> command.

What is alternative to spack?

There are five alternatives to Spack for Linux, Mac, BSD, Self-Hosted solutions and GNU Hurd. The best alternative is Homebrew, which is both free and Open Source. Other great apps like Spack are Flatpak, GNU Guix, Nix Package Manager and Gentoo Prefix.

How do I install spack on Windows?

Installing a package with Spack is very simple. To install a piece of software, simply type spack install <package_name> . Spack can install software either from source or from a binary cache. Packages in the binary cache are signed with GPG for security.

Why do we use DataFrame in spark?

In Spark, a DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood.

How do I update my spack package?

Open a Pull Request against github.com/spack/spack to merge your changes with the spack/spack/develop branch.

What is spark advantage?

Speed. Engineered from the bottom-up for performance, Spark can be 100x faster than Hadoop for large scale data processing by exploiting in memory computing and other optimizations. Spark is also fast when data is stored on disk, and currently holds the world record for large-scale on-disk sorting.

What is spark in Hadoop?

Apache Spark is a lightning-fast cluster computing technology, designed for fast computation. It is based on Hadoop MapReduce and it extends the MapReduce model to efficiently use it for more types of computations, which includes interactive queries and stream processing.

What is the weakness of Spark?

Expensive

While working with Spark, memory consumption is very high. Spark needs huge RAM for processing in-memory. The consumption of memory is very high in Spark which doesn't make it much user-friendly. The additional memory needed to run Spark costs very high which makes Spark expensive.

Why is Spark so powerful?

Speed: Apache Spark helps run applications in the Hadoop cluster up to 100 times faster in memory and 10 times faster on disk. This is due to the ability to reduce the number of reads or write operations to the disk. The intermediate processing data is stored in memory.

Why is Spark better than Hadoop?

Like Hadoop, Spark splits up large tasks across different nodes. However, it tends to perform faster than Hadoop and it uses random access memory (RAM) to cache and process data instead of a file system.